Digital imaging systems and methods of analyzing pixel data of an image of a skin area of a user for determining skin hyperpigmentation

ABSTRACT

Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin hyperpigmentation. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin hyperpigmentation model, trained with the pixel data, is operable to output, across a range of a skin hyperpigmentation scale, skin hyperpigmentation values associated with a degree of skin hyperpigmentation. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin hyperpigmentation model, to determine a user-specific skin hyperpigmentation value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

FIELD OF THE INVENTION

The present disclosure generally relates to digital imaging systems andmethods, and more particularly to digital imaging systems and methods ofanalyzing pixel data of an image of a skin area of a user fordetermining skin hyperpigmentation.

BACKGROUND OF THE INVENTION

Individuals can develop hyper- or hypopigmentation in various spots ontheir bodies. Skin hyperpigmentation describes the quality or state ofskin being darker than the surrounding skin. The skin is the body'slargest organ, and like the other organs, its health, and as a resultits appearance, is affected by various factors including age, exposureto toxins, harsh weather, nutrient deficiencies, and individual habits,such as smoking. Skin hyperpigmentation can be most noticeable to otherswhen it's on an individual's face. But other parts of the head or bodycan also show signs of skin hyperpigmentation as well.

In some instances, highly reactive chemical compounds, generallyreferred to as free radicals, can also contribute to skinhyperpigmentation. Chronic exposure to free radicals can cause damage tohuman cells, tissues, and organs of the body, including the skin. Overtime, exposure to free radicals can cause an individual's skin toproduce more melanin. Deposits of melanin in localized regions of theskin causes the appearance of hyperpigmentation.

In addition, inflammation in the skin can cause deposition of melaninand hence hyperpigmentation. Therefore unmanaged inflammation over achronic period of time can cause risk of hyperpigmentation in the skin Amain cause of skin hyperpigmentation is aging because cumulative UVexposure is known to cause the majority of aging and damage to the skin.

Hyperpigmentation can be avoided through a number of routes. Avoidingskin exposure, by covering skin with clothing or using SPF, can limitthe chance for hyperpigmentation. Treating inflammation in the skin canalso reduce the possibility of hyperpigmentation from occurring.

Use of cosmeceutical products, moisturizers, skin creams, and/or othersuch skin hyperpigmentation products can be used to mitigate theappearance of skin hyperpigmentation. However, such products aretypically differently formulated and/or designed to address differentages, skin types, and/or body areas of a multitude of individuals, wherea given cosmeceutical product, moisturizer, skin cream, and/or othersuch skin hyperpigmentation products product may affect one individualhaving a first set of age and/or otherwise skin hyperpigmentationcharacteristics differently than a second individual having a second setof age and/or otherwise skin hyperpigmentation characteristics. Theproblem is acutely pronounced given the various versions, brands, andtypes of cosmeceutical products, moisturizers, skin creams, and/or othersuch skin hyperpigmentation products currently available to individuals,where each of these different versions, brands, and types of productshave different chemical compositions, ingredients, and/or otherwisedifferent designs or formulations, all of which can vary significantlyin their capability and effectiveness of treating skin hyperpigmentationof a specific individual. This problem is particularly acute becausesuch existing skin hyperpigmentation products—which may be differentlydesigned or formulated—provide little or no feedback or guidance toassist an individual address his or her own personal skinhyperpigmentation issues.

For the foregoing reasons, there is a need for digital imaging systemsand methods of analyzing pixel data of an image of a skin area of a userfor determining skin hyperpigmentation.

SUMMARY OF THE INVENTION

Generally, as described herein, the digital imaging systems and methodsof analyzing pixel data of an image of a skin area of a user fordetermining skin hyperpigmentation, provide a digital imaging, andartificial intelligence (AI), based solution for overcoming problems,whether actual or perceived, that arise from skin hyperpigmentationissues. As described herein, skin hyperpigmentation refers to thequality or state of skin being darker than the surrounding skin.

The digital systems and methods described herein allow a user to submita specific user image to imaging server(s) (e.g., including its one ormore processors), or otherwise a computing device (e.g., such as locallyon the user's mobile device), where the imaging server(s) or usercomputing device implements or executes a skin hyperpigmentation modeltrained with pixel data of potentially 10,000s (or more) images ofindividuals having various degrees of skin hyperpigmentation. The skinhyperpigmentation model may generate, based on a skin hyperpigmentationvalue of a user's skin area, a user-specific electronic recommendationdesigned to address at least one feature identifiable within the pixeldata comprising the at least the portion of the user skin area. Forexample, the at least one feature can comprise pixels or pixel dataindicative of a degree of skin hyperpigmentation, from leasthyperpigmentation to most hyperpigmentation (based on hyperpigmentationvalues across a range of hyperpigmentation values determined in trainingimages of individuals' respective skin areas). In some embodiments, theuser-specific recommendation (and/or product specific recommendation)may be transmitted via a computer network to a user computing device ofthe user for rendering on a display screen. In other embodiments, notransmission to the imaging server of the user's specific image occurs,where the user-specific recommendation (and/or product specificrecommendation) may instead be generated by the skin hyperpigmentationmodel, executing and/or implemented locally on the user's mobile deviceand rendered, by a processor of the mobile device, on a display screenof the mobile device. In various embodiments, such rendering may includegraphical representations, overlays, annotations, and the like foraddressing the feature in the pixel data.

More specifically, as describe herein, a digital imaging method ofanalyzing pixel data of an image of a skin area of a user fordetermining skin hyperpigmentation is disclosed. The digital imagingmethod comprises: (a) aggregating, at one or more processorscommunicatively coupled to one or more memories, a plurality of trainingimages of a plurality of individuals, each of the training imagescomprising pixel data of a skin area of a respective individual; (b)training, by the one or more processors with the pixel data of theplurality of training images, a skin hyperpigmentation model comprisinga skin hyperpigmentation scale and operable to output, across a range ofthe skin hyperpigmentation scale, skin hyperpigmentation valuesassociated with a degree of skin hyperpigmentation ranging from leasthyperpigmentation to most hyperpigmentation; (c) receiving, at the oneor more processors, at least one image of a user, the at least one imagecaptured by a digital camera, and the at least one image comprisingpixel data of at least a portion of a user skin area of the user; (d)analyzing, by the skin hyperpigmentation model executing on the one ormore processors, the at least one image captured by the digital camerato determine a user-specific skin hyperpigmentation value of the userskin area; (e) generating, by the one or more processors based on theuser-specific skin hyperpigmentation value, at least one user-specificelectronic recommendation designed to address at least one featureidentifiable within the pixel data comprising the at least the portionof the user skin area; and (f) rendering, on a display screen of a usercomputing device, the at least one user-specific recommendation.

In addition, as described herein, a digital imaging system is disclosed,configured to analyze pixel data of an image of a skin area of a userfor determining skin hyperpigmentation, the digital imaging systemcomprising: an imaging server comprising a server processor and a servermemory; an imaging application (app) configured to execute on a usercomputing device comprising a device processor and a device memory, theimaging app communicatively coupled to the imaging server; and a skinhyperpigmentation model trained with pixel data of a plurality oftraining images of individuals and operable to output, across a range ofa skin hyperpigmentation scale, skin hyperpigmentation values associatedwith a degree of skin hyperpigmentation ranging from leasthyperpigmentation to most hyperpigmentation, wherein the skinhyperpigmentation model is configured to execute on the server processoror the device processor to cause the server processor or the deviceprocessor to: receive, at the one or more processors, at least one imageof a user, the at least one image captured by a digital camera, and theat least one image comprising pixel data of at least a portion of a userskin area of the user; analyze, by the skin hyperpigmentation modelexecuting on the one or more processors, the at least one image capturedby the digital camera to determine a user-specific skinhyperpigmentation value of the user skin area; generate, by the one ormore processors based on the user-specific skin hyperpigmentation value,at least one user-specific electronic recommendation designed to addressat least one feature identifiable within the pixel data comprising theat least the portion of the user skin area; and render, on a displayscreen of a user computing device, the at least one user-specificrecommendation.

Further, as described herein, a tangible, non-transitorycomputer-readable medium storing instructions for analyzing pixel dataof an image of a skin area of a user for determining skinhyperpigmentation is disclosed. The instructions, when executed by oneor more processors may cause the one or more processors to: (a)aggregate, at one or more processors communicatively coupled to one ormore memories, a plurality of training images of a plurality ofindividuals, each of the training images comprising pixel data of a skinarea of a respective individual; (b) train, by the one or moreprocessors with the pixel data of the plurality of training images, askin hyperpigmentation model comprising a skin hyperpigmentation scaleand operable to output, across a range of the skin hyperpigmentationscale, skin hyperpigmentation values associated with a degree of skinhyperpigmentation ranging from least hyperpigmentation to mosthyperpigmentation; (c) receive, at the one or more processors, at leastone image of a user, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of a user skin area of the user; (d) analyze, by the skinhyperpigmentation model executing on the one or more processors, the atleast one image captured by the digital camera to determine auser-specific skin hyperpigmentation value of the user skin area; (e)generate, by the one or more processors based on the user-specific skinhyperpigmentation value, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the userskin area; and (f) render, on a display screen of a user computingdevice, the at least one user-specific recommendation.

In accordance with the above, and with the disclosure herein, thepresent disclosure includes improvements in computer functionality or inimprovements to other technologies at least because the disclosuredescribes that, e.g., an imaging server, or otherwise computing device(e.g., a user computer device), is improved where the intelligence orpredictive ability of the imaging server or computing device is enhancedby a trained (e.g., machine learning trained) skin hyperpigmentationmodel. The skin hyperpigmentation model, executing on the imaging serveror computing device, is able to accurately identify, based on pixel dataof other individuals, a user-specific skin hyperpigmentation value forat least a portion of a user skin area and a user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data of a specific user comprising the at least theportion of the user skin area. That is, the present disclosure describesimprovements in the functioning of the computer itself or “any othertechnology or technical field” because an imaging server or usercomputing device is enhanced with a plurality of training images (e.g.,10,000s of training images and related pixel data as feature data) toaccurately predict, detect, or determine pixel data of a user-specificimages, such as newly provided customer images. This improves over theprior art at least because existing systems lack such predictive orclassification functionality and are simply not capable of accuratelyanalyzing user-specific images to output a predictive result to addressat least one feature (e.g., related to skin hyperpigmentation)identifiable within the pixel data comprising the at least the portionof the user skin area.

For similar reasons, the present disclosure relates to improvement toother technologies or technical fields at least because the presentdisclosure describes or introduces improvements to computing devices inthe field(s) of skin hyperpigmentation and/or dermatology, whereby thetrained skin hyperpigmentation model executing on the imaging device(s)or computing devices improve the field(s) of skin hyperpigmentationand/or dermatology with digital and/or artificial intelligence basedanalysis of user or individual images to output a predictive result toaddress user-specific pixel data of at least one feature identifiablewithin the pixel data comprising the at least the least the portion ofthe user skin area.

In addition, the present disclosure includes specific features otherthan what is well-understood, routine, conventional activity in thefield, or adding unconventional steps that confine the claim to aparticular useful application, e.g., analyzing pixel data of an image ofa skin area of a user for determining skin hyperpigmentation asdescribed herein.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred embodiments whichhave been shown and described by way of illustration. As will berealized, the present embodiments may be capable of other and differentembodiments, and their details are capable of modification in variousrespects. Accordingly, the drawings and description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an example digital imaging system configured toanalyze pixel data of an image of a skin area of a user for determiningskin hyperpigmentation, in accordance with various embodiments disclosedherein.

FIG. 2A illustrates an example image and its related pixel data that maybe used for training and/or implementing a skin hyperpigmentation model,in accordance with various embodiments disclosed herein.

FIG. 2B illustrates a further example image and its related pixel datathat may be used for training and/or implementing a skinhyperpigmentation model, in accordance with various embodimentsdisclosed herein.

FIG. 2C illustrates a further example image and its related pixel datathat may be used for training and/or implementing a skinhyperpigmentation model, in accordance with various embodimentsdisclosed herein.

FIG. 3 illustrates a diagram of a digital imaging method of analyzingpixel data of an image of a skin area of a user for determining skinhyperpigmentation, in accordance with various embodiments disclosedherein.

FIG. 4 illustrates an example user interface as rendered on a displayscreen of a user computing device in accordance with various embodimentsdisclosed herein.

The Figures depict preferred embodiments for purposes of illustrationonly. Alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an example digital imaging system 100 configured toanalyze pixel data of an image (e.g., any one or more of images 202 a,202 b, and/or 202 c) of a skin area, or otherwise body or body area, ofa user for determining skin hyperpigmentation, in accordance withvarious embodiments disclosed herein. As referred to herein, a “body”may refer to any portion of the human body including the torso, waist,face, head, arm, leg, or other appendage or portion or part of the bodythereof. In the example embodiment of FIG. 1 , digital imaging system100 includes server(s) 102, which may comprise one or more computerservers. In various embodiments server(s) 102 comprise multiple servers,which may comprise a multiple, redundant, or replicated servers as partof a server farm. In still further embodiments, server(s) 102 may beimplemented as cloud-based servers, such as a cloud-based computingplatform. For example, imaging server(s) 102 may be any one or morecloud-based platform(s) such as MICROSOFT AZURE, AMAZON AWS, or thelike. Server(s) 102 may include one or more processor(s) 104 as well asone or more computer memories 106. Server(s) 102 may be referred toherein as “imaging server(s).”

The memories 106 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers. The memorie(s) 106 may store an operating system (OS) (e.g.,Microsoft Windows, Linux, UNIX, etc.) capable of facilitating thefunctionalities, apps, methods, or other software as discussed herein.The memorie(s) 106 may also store a skin hyperpigmentation model 108,which may be an artificial intelligence based model, such as a machinelearning model, trained on various images (e.g., images 202 a, 202 b,and/or 202 c), as described herein. Additionally, or alternatively, theskin hyperpigmentation model 108 may also be stored in database 105,which is accessible or otherwise communicatively coupled to imagingserver(s) 102. The memories 106 may also store machine readableinstructions, including any of one or more application(s), one or moresoftware component(s), and/or one or more application programminginterfaces (APIs), which may be implemented to facilitate or perform thefeatures, functions, or other disclosure described herein, such as anymethods, processes, elements or limitations, as illustrated, depicted,or described for the various flowcharts, illustrations, diagrams,figures, and/or other disclosure herein. For example, at least some ofthe applications, software components, or APIs may be, include,otherwise be part of, an imaging based machine learning model orcomponent, such as the skin hyperpigmentation model 108, where each maybe configured to facilitate their various functionalities discussedherein. It should be appreciated that one or more other applications maybe envisioned and that are executed by the processor(s) 104.

The processor(s) 104 may be connected to the memories 106 via a computerbus responsible for transmitting electronic data, data packets, orotherwise electronic signals to and from the processor(s) 104 andmemories 106 in order to implement or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein.

The processor(s) 104 may interface with the memory 106 via the computerbus to execute the operating system (OS). The processor(s) 104 may alsointerface with the memory 106 via the computer bus to create, read,update, delete, or otherwise access or interact with the data stored inthe memories 106 and/or the database 104 (e.g., a relational database,such as Oracle, DB2, MySQL, or a NoSQL based database, such as MongoDB).The data stored in the memories 106 and/or the database 105 may includeall or part of any of the data or information described herein,including, for example, training images and/or user images (e.g., eitherof which including any one or more of images 202 a, 202 b, and/or 202 c)or other information of the user, including demographic, age, race, skintype, or the like.

The imaging server(s) 102 may further include a communication componentconfigured to communicate (e.g., send and receive) data via one or moreexternal/network port(s) to one or more networks or local terminals,such as computer network 120 and/or terminal 109 (for rendering orvisualizing) described herein. In some embodiments, imaging server(s)102 may include a client-server platform technology such as ASP.NET,Java J2EE, Ruby on Rails, Node.js, a web service or online API,responsive for receiving and responding to electronic requests. Theimaging server(s) 102 may implement the client-server platformtechnology that may interact, via the computer bus, with the memories(s)106 (including the applications(s), component(s), API(s), data, etc.stored therein) and/or database 105 to implement or perform the machinereadable instructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein.According to some embodiments, the imaging server(s) 102 may include, orinteract with, one or more transceivers (e.g., WWAN, WLAN, and/or WPANtransceivers) functioning in accordance with IEEE standards, 3GPPstandards, or other standards, and that may be used in receipt andtransmission of data via external/network ports connected to computernetwork 120. In some embodiments, computer network 120 may comprise aprivate network or local area network (LAN). Additionally, oralternatively, computer network 120 may comprise a public network suchas the Internet.

Imaging server(s) 102 may further include or implement an operatorinterface configured to present information to an administrator oroperator and/or receive inputs from the administrator or operator. Asshown in FIG. 1 , an operator interface may provide a display screen(e.g., via terminal 109). Imaging server(s) 102 may also provide I/Ocomponents (e.g., ports, capacitive or resistive touch sensitive inputpanels, keys, buttons, lights, LEDs), which may be directly accessiblevia or attached to imaging server(s) 102 or may be indirectly accessiblevia or attached to terminal 109. According to some embodiments, anadministrator or operator may access the server 102 via terminal 109 toreview information, make changes, input training data or images, and/orperform other functions.

As described above herein, in some embodiments, imaging server(s) 102may perform the functionalities as discussed herein as part of a “cloud”network or may otherwise communicate with other hardware or softwarecomponents within the cloud to send, retrieve, or otherwise analyze dataor information described herein.

In general, a computer program or computer based product, application,or code (e.g., the model(s), such as AI models, or other computinginstructions described herein) may be stored on a computer usablestorage medium, or tangible, non-transitory computer-readable medium(e.g., standard random access memory (RAM), an optical disc, a universalserial bus (USB) drive, or the like) having such computer-readableprogram code or computer instructions embodied therein, wherein thecomputer-readable program code or computer instructions may be installedon or otherwise adapted to be executed by the processor(s) 104 (e.g.,working in connection with the respective operating system in memories106) to facilitate, implement, or perform the machine readableinstructions, methods, processes, elements or limitations, asillustrated, depicted, or described for the various flowcharts,illustrations, diagrams, figures, and/or other disclosure herein. Inthis regard, the program code may be implemented in any desired programlanguage, and may be implemented as machine code, assembly code, bytecode, interpretable source code or the like (e.g., via Golang, Python,C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML,CSS, XML, etc.).

As shown in FIG. 1 , imaging server(s) 102 are communicativelyconnected, via computer network 120 to the one or more user computingdevices 111 c 1-111 c 3 and/or 112 c 1-112 c 3 via base stations 111 band 112 b. In some embodiments, base stations 111 b and 112 b maycomprise cellular base stations, such as cell towers, communicating tothe one or more user computing devices 111 c 1-111 c 3 and 112 c 1-112 c3 via wireless communications 121 based on any one or more of variousmobile phone standards, including NMT, GSM, CDMA, UMMTS, LTE, 5G, or thelike. Additionally or alternatively, base stations 111 b and 112 b maycomprise routers, wireless switches, or other such wireless connectionpoints communicating to the one or more user computing devices 111 c1-111 c 3 and 112 c 1-112 c 3 via wireless communications 122 based onany one or more of various wireless standards, including by non-limitingexample, IEEE 802.11a/b/c/g (WIFI), the BLUETOOTH standard, or the like.

Any of the one or more user computing devices 111 c 1-111 c 3 and/or 112c 1-112 c 3 may comprise mobile devices and/or client devices foraccessing and/or communications with imaging server(s) 102. In variousembodiments, user computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c3 may comprise a cellular phone, a mobile phone, a tablet device, apersonal data assistance (PDA), or the like, including, by non-limitingexample, an APPLE iPhone or iPad device or a GOOGLE ANDROID based mobilephone or table. In still further embodiments, user computing devices 111c 1-111 c 3 and/or 112 c 1-112 c 3 may comprise a home assistant deviceand/or personal assistant device, e.g., having display screens,including, by way of non-limiting example, any one or more of a GOOGLEHOME device, an AMAZON ALEXA device, an ECHO SHOW device, or the like.In additional embodiments, user computing devices 111 c 1-111 c 3 and/or112 c 1-112 c 3 may comprise a retail computing device. A retailcomputing device would be configured in the same or similar manner,e.g., as described herein for user computing devices 111 c 1-111 c 3,including having a processor and memory, for implementing, orcommunicating with (e.g., via server(s) 102), a skin hyperpigmentationmodel 108 as described herein. However, a retail computing device may belocated, installed, or otherwise positioned within a retail environmentto allow users and/or customers of the retail environment to utilize thedigital imaging systems and methods on site within the retailenvironment. For example, the retail computing device may be installedwithin a kiosk for access by a user. The user may then upload ortransfer images (e.g., from a user mobile device) to the kiosk toimplement the digital imaging systems and methods described herein.Additionally, or alternatively, the kiosk may be configured with acamera to allow the user to take new images (e.g., in a private mannerwhere warranted) of himself or herself for upload and transfer. In suchembodiments, the user or consumer himself or herself would be able touse the retail computing device to receive and/or have rendered auser-specific electronic recommendation, as described herein, on adisplay screen of the retail computing device. Additionally, oralternatively, the retail computing device may be a mobile device (asdescribed herein) as carried by an employee or other personnel of theretail environment for interacting with users or consumers on site. Insuch embodiments, a user or consumer may be able to interact with anemployee or otherwise personnel of the retail environment, via theretail computing device (e.g., by transferring images from a mobiledevice of the user to the retail computing device or by capturing newimages by a camera of the retail computing device), to receive and/orhave rendered a user-specific electronic recommendation, as describedherein, on a display screen of the retail computing device. In addition,the one or more user computing devices 111 c 1-111 c 3 and/or 112 c1-112 c 3 may implement or execute an operating system (OS) or mobileplatform such as Apple's iOS and/or Google's Android operation system.Any of the one or more user computing devices 111 c 1-111 c 3 and/or 112c 1-112 c 3 may comprise one or more processors and/or one or morememories for storing, implementing, or executing computing instructionsor code, e.g., a mobile application or a home or personal assistantapplication, as described in various embodiments herein. As shown inFIG. 1 , skin hyperpigmentation model 108 may also be stored locally ona memory of a user computing device (e.g., user computing device 111 c1).

User computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c 3 maycomprise a wireless transceiver to receive and transmit wirelesscommunications 121 and/or 122 to and from base stations 111 b and/or 112b. Pixel based images 202 a, 202 b, and/or 202 c may be transmitted viacomputer network 120 to imaging server(s) 102 for training of model(s)and/or imaging analysis as describe herein.

In addition, the one or more user computing devices 111 c 1-111 c 3and/or 112 c 1-112 c 3 may include a digital camera and/or digital videocamera for capturing or taking digital images and/or frames (e.g., whichcan be any one or more of images 202 a, 202 b, and/or 202 c). Eachdigital image may comprise pixel data for training or implementingmodel(s), such as AI or machine learning models, as described herein.For example, a digital camera and/or digital video camera of, e.g., anyof user computing devices 111 c 1-111 c 3 and/or 112 c 1-112 c 3, may beconfigured to take, capture, or otherwise generate digital images (e.g.,pixel based images 202 a, 202 b, and/or 202 c) and, at least in someembodiments, may store such images in a memory of a respective usercomputing devices.

Still further, each of the one or more user computer devices 111 c 1-111c 3 and/or 112 c 1-112 c 3 may include a display screen for displayinggraphics, images, text, product recommendations, data, pixels, features,and/or other such visualizations or information as described herein. Invarious embodiments, graphics, images, text, product recommendations,data, pixels, features, and/or other such visualizations or informationmay be received by imaging server(s) 102 for display on the displayscreen of any one or more of user computer devices 111 c 1-111 c 3and/or 112 c 1-112 c 3. Additionally, or alternatively, a user computerdevice may comprise, implement, have access to, render, or otherwiseexpose, at least in part, an interface or a guided user interface (GUI)for displaying text and/or images on its display screen.

FIGS. 2A-2C illustrate example images 202 a, 202 b, and 202 c that maybe collected or aggregated at imaging server(s) 102 and may be analyzedby, and/or used to train, a skin hyperpigmentation model (e.g., an AImodel such as a machine learning imaging model as describe herein). Eachof these images may comprise pixel data (e.g., RGB data) correspondingrepresenting feature data and corresponding to each of the personalattributes of the respective users 202 au, 202 bu, and 202 cu, withinthe respective image. The pixel data may be captured by a digital cameraof one of the user computing devices (e.g., one or more user computerdevices 111 c 1-111 c 3 and/or 112 c 1-112 c 3).

Generally, as described herein, pixel data (e.g., pixel data 202 ap, 202bp, and/or 202 cp) comprises individual points or squares of data withinan image, where each point or square represents a single pixel (e.g.,pixel 202 ap 1 and pixel 202 ap 2) within an image. Each pixel may be aspecific location within an image. In addition, each pixel may have aspecific color (or lack thereof). Pixel color may be determined by acolor format and related channel data associated with a given pixel. Forexample, a popular color format includes the red-green-blue (RGB) formathaving red, green, and blue channels. That is, in the RGB format, dataof a pixel is represented by three numerical RGB components (Red, Green,Blue), that may be referred to as a channel data, to manipulate thecolor of pixel's area within the image. In some implementations, thethree RGB components may be represented as three 8-bit numbers for eachpixel. Three 8-bit bytes (one byte for each of RGB) is used to generate24 bit color. Each 8-bit RGB component can have 256 possible values,ranging from 0 to 255 (i.e., in the base 2 binary system, an 8 bit bytecan contain one of 256 numeric values ranging from 0 to 255). Thischannel data (R, G, and B) can be assigned a value from 0 255 and beused to set the pixel's color. For example, three values like (250, 165,0), meaning (Red=250, Green=165, Blue=0), can denote one Orange pixel.As a further example, (Red=255, Green=255, Blue=0) means Red and Green,each fully saturated (255 is as bright as 8 bits can be), with no Blue(zero), with the resulting color being Yellow. As a still furtherexample, the color black has an RGB value of (Red=0, Green=0, Blue=0)and white has an RGB value of (Red=255, Green=255, Blue=255). Gray hasthe property of having equal or similar RGB values. So (Red=220,Green=220, Blue=220) is a light gray (near white), and (Red=40,Green=40, Blue=40) is a dark gray (near black).

In this way, the composite of three RGB values creates the final colorfor a given pixel. With a 24-bit RGB color image using 3 bytes there canbe 256 shades of red, and 256 shades of green, and 256 shades of blue.This provides 256×256×256, i.e., 16.7 million possible combinations orcolors for 24 bit RGB color images. In this way, the pixel's RGB datavalue shows how much of each of Red, and Green, and Blue pixel iscomprised of. The three colors and intensity levels are combined at thatimage pixel, i.e., at that pixel location on a display screen, toilluminate a display screen at that location with that color. In is tobe understood, however, that other bit sizes, having fewer or more bits,e.g., 10-bits, may be used to result in fewer or more overall colors andranges.

As a whole, the various pixels, positioned together in a grid pattern,form a digital image (e.g., pixel data 202 ap, 202 bp, and/or 202 cp). Asingle digital image can comprise thousands or millions of pixels.Images can be captured, generated, stored, and/or transmitted in anumber of formats, such as JPEG, TIFF, PNG and GIF. These formats usepixels to store represent the image.

FIG. 2A illustrates an example image 202 a and its related pixel data(e.g., pixel data 202 ap) that may be used for training and/orimplementing a skin hyperpigmentation model (e.g., skinhyperpigmentation model 108), in accordance with various embodimentsdisclosed herein. Example image 202 a illustrates a user skin area ofuser 202 au or individual at a body area location comprising the user'sneck. Image 202 a is comprised of pixel data, including pixel data 202ap. Pixel data 202 ap includes a plurality of pixels including pixel 202ap 1 and pixel 202 ap 2. Pixel 202 ap 2 is a pixel positioned in image202 a comprising a body area location of the user, including the user'schin or cheek. Pixel 202 ap 1 is a dark pixel (e.g., a pixel with low R,G, and B values) positioned in image 202 a from the body area location(e.g., chin or cheek, e.g., of pixel 202 ap 2) as identifiable withinthe portion of the user skin area of pixel data 202 ap. Pixel data 202ap includes various remaining pixels including remaining portions ofuser 202 au, including other body area location(s) (e.g., check, neck,head, etc.). Pixel data 202 ap further includes pixels representingfurther features including the user's position, posture, body portions,and other features as shown in FIG. 2A.

FIG. 2B illustrates a further example image 202 b and its related pixeldata (e.g., pixel data 202 bp) that may be used for training and/orimplementing a skin hyperpigmentation model (e.g., skinhyperpigmentation model 108), in accordance with various embodimentsdisclosed herein. Example image 202 b illustrates a user skin area ofuser 202 bu or individual at a body area location comprising the user'sarm. Image 202 b is comprised of pixel data, including pixel data 202bp. Pixel data 202 bp includes a plurality of pixels including pixel 202bp 1 and pixel 202 bp 2. Pixel 202 bp 1 is a pixel positioned in image202 b comprising a body area location of the user, including the user'sarm. Pixel 202 bp 2 is a lighter pixel (e.g., a pixel with high R, G,and B values) positioned in image 202 b where user 202 bu has a roughamount of skin from the body area location (e.g., arm, e.g., of pixel202 bp 1) identifiable within the portion of the user skin area of pixeldata 202 bp. Pixel data 202 bp further includes pixels representingfurther features including the user's shoulder, elbow, forearm, posture,body portions, and other features as shown in FIG. 2B.

FIG. 2C illustrates a further example image 202 cu and its related pixeldata (e.g., 202 cp) that may be used for training and/or implementing askin hyperpigmentation model (e.g., skin hyperpigmentation model 108),in accordance with various embodiments disclosed herein. Example image202 c illustrates a user skin area of user 202 cu or individual at abody area location comprising the user's head or face, and, inparticular, eye. Image 202 c is comprised of pixel data, including pixeldata 202 cp. Pixel data 202 cp includes a plurality of pixels includingpixel 202 cp 1 and pixel 202 cp 2. Pixel 202 cp 2 is a pixel positionedin image 202 c comprising a body area location of the user, includingthe user's head or face, and, in particular, eye. Pixel 202 cp 1 is adark pixel (e.g., a pixel with low R, G, and B values) positioned inimage 202 c where user 202 cu has a dark amount of skin from the bodyarea location (e.g., head, face, or eye, e.g., of pixel 202 cp 2)identifiable within the portion of the user skin area of pixel data 202cp. Pixel data 202 cp includes various remaining pixels includingremaining portions of user 202 cu, including other body area location(s)(e.g., check, neck, etc.). Pixel data 202 cp further includes pixelsrepresenting further features including the user's position, posture,body portions, and other features as shown in FIG. 2C.

FIG. 3 illustrates a diagram of a digital imaging method 300 ofanalyzing pixel data of an image (e.g., any of images 202 a, 202 b,and/or 202 c) of a skin area of a user for determining skinhyperpigmentation, in accordance with various embodiments disclosedherein. Images, as described herein, are generally pixel images ascaptured by a digital camera (e.g., a digital camera of user computingdevice 111 c 1). In some embodiments an image may comprise or refer to aplurality of images such as a plurality of images (e.g., frames) ascollected using a digital video camera. Frames comprise consecutiveimages defining motion, and can comprise a movie, a video, or the like.

At block 302, method 300 comprises aggregating, at one or moreprocessors communicatively coupled to one or more memories, a pluralityof training images of a plurality of individuals, each of the trainingimages comprising pixel data of a skin area of a respective individual.

At block 304, method 300 comprises training, by the one or moreprocessors with the pixel data of the plurality of training images, askin hyperpigmentation model (e.g., skin hyperpigmentation model 108)comprising a skin hyperpigmentation scale and operable to output, acrossa range of the skin hyperpigmentation scale, skin hyperpigmentationvalues associated with a degree of skin hyperpigmentation ranging fromleast hyperpigmentation to most hyperpigmentation. In variousembodiments, a skin hyperpigmentation scale can be an internalized scaleor otherwise custom scale, unique to the skin hyperpigmentation model,where a least or small hyperpigmentation value may be determined from animage or set of images having skin areas with low skin hyperpigmentationvalues, i.e., images where the pixel data (e.g., lighter pixel datahaving higher RGB value(s)) indicates that a skin area is rough across askin area of the user. Similarly, a most or large hyperpigmentationvalue may be determined from an image or set of images having skin areaswith high skin hyperpigmentation values, i.e., images where the pixeldata (e.g., darker pixel data having lower RGB value(s)) indicates thata skin area is rough in a skin area of the user. Additionally, oralternatively, skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) is trained to detect patterns or groups ofpixels within a given image. Such patterns or groups of pixels may bedetermined as having the same or similar RGB values (e.g., homogenousvalues) in similar areas or portions of the given image. For example, apattern or group of pixels may have similar RGB values along one or morebody area location(s), including, for example, a jawline, arm, othersuch body portion of a user having curves or contours. Such curves orcontours, identifiable within the pixel data, may track along theunderlying bone or muscle tissue of a user, which in an image of theuser, are expressed as the patterns or groups of pixels having the sameor similar RGB values (e.g., homogenous values) for a given portion ofthe image. In such instances, the patterns or groups of pixels canindicate a given body area location (e.g., a jawline), where auser-specific skin hyperpigmentation value may be determined based onthe patterns or groups pixels. For example, in some embodiments, ahyperpigmentation of skin can be determined from a body area location(e.g., as determined from the patterns or groups of pixels).Additionally, or alternatively, a grouping or pattern of the pixels forthe body area location itself can suggest skin hyperpigmentation. Forexample, a tighter grouping or pattern of pixels may indicate roughskin, but a looser grouping or pattern may indicate smooth skin.

In some embodiments, the skin hyperpigmentation scale may be apercentage scale, e.g., with skin hyperpigmentation model outputtingskin hyperpigmentation values from 0% to 100%, where 0% represents leasthyperpigmentation and 100% represents most hyperpigmentation. Values canrange across this scale where a skin hyperpigmentation value of 67%represents one or more pixels of a skin area detected within an imagethat has a higher skin hyperpigmentation value than a skinhyperpigmentation value of 10% as detected for one or more pixels of askin area within the same image or a different image (of the same ordifferent user).

In some embodiments, the skin hyperpigmentation scale may be a numericalor decimal based scale, e.g., with skin hyperpigmentation modeloutputting skin hyperpigmentation values, e.g., from 0 to 10, where 0represents least hyperpigmentation and 10 represents mosthyperpigmentation. Values can range across this scale where a skinhyperpigmentation value of 78.9 represents one or more pixels of a skinarea detected within an image that has a higher skin hyperpigmentationvalue than a skin hyperpigmentation value of 21.3 as detected for one ormore pixels of a skin area within the same image or a different image(of the same or different user).

Skin hyperpigmentation values may be determined at the pixel level orfor a given skin area (e.g., one or more pixels) in an image.Additionally, or alternatively, a comprehensive skin hyperpigmentationvalue, which can be a user-specific skin hyperpigmentation value asdescribed herein, may be determined by averaging (or otherwisestatistically analyzing) skin hyperpigmentation values for one or morepixels of a given skin area.

In various embodiments, skin hyperpigmentation model is an artificialintelligence (AI) based model trained with at least one AI algorithm.Training of skin hyperpigmentation model 108 involves image analysis ofthe training images to configure weights of skin hyperpigmentation model108, and its underlying algorithm (e.g., machine learning or artificialintelligence algorithm) used to predict and/or classify future images.For example, in various embodiments herein, generation of skinhyperpigmentation model 108 involves training skin hyperpigmentationmodel 108 with the plurality of training images of a plurality ofindividuals, where each of the training images comprise pixel data of askin area of a respective individual. In some embodiments, one or moreprocessors of a server or a cloud-based computing platform (e.g.,imaging server(s) 102) may receive the plurality of training images ofthe plurality of individuals via a computer network (e.g., computernetwork 120). In such embodiments, the server and/or the cloud-basedcomputing platform may train the skin hyperpigmentation model with thepixel data of the plurality of training images.

In various embodiments, a machine learning imaging model, as describedherein (e.g., skin hyperpigmentation model 108), may be trained using asupervised or unsupervised machine learning program or algorithm. Themachine learning program or algorithm may employ a neural network, whichmay be a convolutional neural network, a deep learning neural network,or a combined learning module or program that learns in two or morefeatures or feature datasets (e.g., pixel data) in a particular areas ofinterest. The machine learning programs or algorithms may also includenatural language processing, semantic analysis, automatic reasoning,regression analysis, support vector machine (SVM) analysis, decisiontree analysis, random forest analysis, K-Nearest neighbor analysis,naïve Bayes analysis, clustering, reinforcement learning, and/or othermachine learning algorithms and/or techniques. In some embodiments, theartificial intelligence and/or machine learning based algorithms may beincluded as a library or package executed on imaging server(s) 102. Forexample, libraries may include the TENSORFLOW based library, the PYTORCHlibrary, and/or the SCIKIT-LEARN Python library.

Machine learning may involve identifying and recognizing patterns inexisting data (such as training a model based on pixel data withinimages having pixel data of a skin area of a respective individual) inorder to facilitate making predictions or identification for subsequentdata (such as using the model on new pixel data of a new individual inorder to determine a user-specific skin hyperpigmentation value of theuser skin area of a user).

Machine learning model(s), such as the skin hyperpigmentation modeldescribed herein for some embodiments, may be created and trained basedupon example data (e.g., “training data” and related pixel data) inputsor data (which may be termed “features” and “labels”) in order to makevalid and reliable predictions for new inputs, such as testing level orproduction level data or inputs. In supervised machine learning, amachine learning program operating on a server, computing device, orotherwise processor(s), may be provided with example inputs (e.g.,“features”) and their associated, or observed, outputs (e.g., “labels”)in order for the machine learning program or algorithm to determine ordiscover rules, relationships, patterns, or otherwise machine learning“models” that map such inputs (e.g., “features”) to the outputs (e.g.,labels), for example, by determining and/or assigning weights or othermetrics to the model across its various feature categories. Such rules,relationships, or otherwise models may then be provided subsequentinputs in order for the model, executing on the server, computingdevice, or otherwise processor(s), to predict, based on the discoveredrules, relationships, or model, an expected output.

In unsupervised machine learning, the server, computing device, orotherwise processor(s), may be required to find its own structure inunlabeled example inputs, where, for example multiple trainingiterations are executed by the server, computing device, or otherwiseprocessor(s) to train multiple generations of models until asatisfactory model, e.g., a model that provides sufficient predictionaccuracy when given test level or production level data or inputs, isgenerated. The disclosures herein may use one or both of such supervisedor unsupervised machine learning techniques.

Image analysis may include training a machine learning based model(e.g., the skin hyperpigmentation model) on pixel data of images of oneor more individuals comprising pixel data of respective skin areas ofthe one or more individuals. Additionally, or alternatively, imageanalysis may include using a machine learning imaging model, aspreviously trained, to determine, based on the pixel data (e.g.,including their RGB values) one or more images of the individual(s), auser-specific skin hyperpigmentation value of the user skin area. Theweights of the model may be trained via analysis of various RGB valuesof individual pixels of a given image. For example, dark or low RGBvalues (e.g., a pixel with values R=25, G=28, B=31) may indicate ahyperpigmentation of skin from body area location(s) (e.g., check, neck,head, etc.) of a user. A red toned RGB value (e.g., a pixel with valuesR=215, G=90, B=85) may indicate irritated skin. A lighter RGB values(e.g., a pixel with R=181, G=170, and B=191) may indicate a lightervalue, such as a normal skin tone color. Together, when a pixel withskin toned RGB value and/or a pixel with a lighter higher RGB value ispositioned within a given image, or is otherwise surrounded by, a groupor set of pixels having skin toned colors, then that may indicate anarea on the skin where stretching of the skin occurs, respectively, asidentified within the given image. In this way, pixel data (e.g.,detailing one or more features of an individual, such as user skinarea(s) of various individuals having different specific skinhyperpigmentation values(s) within 10,000s training images may be usedto train or use a machine learning imaging model to determine auser-specific skin hyperpigmentation value of a given user skin area.

In some embodiments training, by the one or more processors (e.g., ofimaging server(s) 102) with the pixel data of the plurality of trainingimages, the skin hyperpigmentation model (e.g., skin hyperpigmentationmodel 108) comprises training the skin hyperpigmentation model (e.g.,skin hyperpigmentation model 108) to detect a hyperpigmentation of skinfrom a body area location of the user to determine the user-specificskin hyperpigmentation value of the user skin area. In such embodimentsthe skin hyperpigmentation model may be trained to recognize that pixelswith lighter values (e.g., lighter or higher RGB values) indicate ahyperpigmentation of skin from body area location(s) (e.g., an arm) of auser. For example, pixel 202 bp 1 is a pixel positioned in image 202 bcomprising a body area location of the user, including the user's arm.Pixel 202 bp 2 is a lighter pixel (e.g., a pixel with high R, G, and Bvalues) positioned in image 202 b where user 202 bu has ahyperpigmentation of skin from the body area location (e.g., arm, e.g.,of pixel 202 bp 1) identifiable within the portion of the user skin areaof pixel data 202 bp. Skin hyperpigmentation model 108 may be trained torecognize (by assigning greater weighs to lighter pixels) that suchlighter pixels (e.g., pixel 202 bp 2) against a pixel or group pixelshaving type skin tone colors (e.g., pixel 202 bp 1) indicates thathyperpigmentation of skin from the body area location occurs. The amountof hyperpigmentation can be determined from the amount or count ofpixels detected from the lighter pixels to the body area location. Forexample, skin hyperpigmentation model 108 may be trained to recognize(by assigning greater weighs to pixels within a zone between the lighterpixels and the body area location) that such zone (e.g., between orincluding pixels 202 bp 1 and 202 bp 2) represents an amount of skinfrom the body area location (e.g., arm). In this way the skinhyperpigmentation model can identify patterns within the pixel data todetermine a user-specific skin hyperpigmentation value of the user skinarea.

Additionally, or alternatively, training, by the one or more processors(e.g., of imaging server(s) 102) with the pixel data of the plurality oftraining images, the skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) may comprise training the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108) todetect a darker amount of skin within the skin area to determine theuser-specific skin hyperpigmentation value of the user skin area. Insuch embodiments the skin hyperpigmentation model may be trained torecognize that pixels with darker values (e.g., darker or lower RGBvalues) indicate a darker amount of skin within the skin area of a user.For example, pixel 202 ap 2 is a pixel positioned in image 202 acomprising a body area location of the user, including the user's chinor cheek. Pixel 202 ap 1 is a dark pixel (e.g., a pixel with low R, G,and B values) positioned in image 202 a where user 202 au has a darkamount of skin identifiable within the portion of the user skin area ofpixel data 202 ap. Skin hyperpigmentation model 108 may be trained torecognize (by assigning greater weighs to darker pixels) that suchdarker pixels (e.g., pixel 202 ap 1) against a pixel or group pixelshaving skin tone colors indicates that a darker amount of skin occurs.The amount of hyperpigmentation can be determined from the amount orcount of pixels detected from the dark pixels of the user skin area. Forexample, skin hyperpigmentation model 108 may be trained to recognize(by assigning greater weights to pixels within darker weights in apattern across skin tone colors) that such pattern (e.g., of 202 ap 1)represents or is a swelling amount of skin from in the user skin area.In this way the skin hyperpigmentation model can identify patternswithin the pixel data to determine a user-specific skinhyperpigmentation value of the user skin area.

Training, by the one or more processors (e.g., imaging server(s) 102)with the pixel data of the plurality of training images, the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108) maycomprise training the skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) to detect a hyperpigmentation of skin froma body area location of the user within the skin area (as describedherein) to determine the user-specific skin hyperpigmentation value ofthe user skin area.

In various embodiments, a skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) may be further trained, by one or moreprocessors (e.g., imaging server(s) 102), with the pixel data of theplurality of training images, to output one or more location identifiersindicating one or more corresponding body area locations of respectiveindividuals. In such embodiments, the skin hyperpigmentation model(e.g., skin hyperpigmentation model 108), executing on the one or moreprocessors (e.g., imaging server(s) 102) and analyzing the at least oneimage of the user, can determine a location identifier indicating a bodyarea location of the user's skin area. For example, body area locationsmay comprise a user's cheek, a user's neck, a user's head, a user'sgroin, a user's underarm, a user's chest, a user's back, a user's leg, auser's arm, or a user's bikini area. For example, each of images 202 a,202 b, and 202 c illustrate example body area locations including auser's neck, a user's arm, and a user's face, head, or eye,respectively.

With reference to FIG. 3 , at block 306 method 300 comprises receiving,at the one or more processors (e.g., imaging server(s) 102 and/or a usercomputing device, such as user computing device 111 c 1), at least oneimage of a user. The at least one image may have been captured by adigital camera. In addition, the at least one image may comprise pixeldata of at least a portion of a user skin area of the user.

At block 308, method 300 comprises analyzing, by the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108)executing on the one or more processors (e.g., imaging server(s) 102and/or a user computing device, such as user computing device 111 c 1),the at least one image captured by the digital camera to determine auser-specific skin hyperpigmentation value of the user skin area.

At block 310, method 300 comprises generating, by the one or moreprocessors (e.g., imaging server(s) 102 and/or a user computing device,such as user computing device 111 c 1) based on the user-specific skinhyperpigmentation value, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the userskin area.

At block 312, method 300 comprises rendering, on a display screen of auser computing device, the at least one user-specific recommendation. Auser computing device may comprise at least one of a mobile device, atablet, a handheld device, or a desktop device, for example, asdescribed herein for FIG. 1 . In some embodiments, the user computingdevice (e.g., user computing device 111 c 1) may receive the at leastone image comprising the pixel data of the at least the portion of theuser skin area. In such embodiments, the user computing device mayexecute the skin hyperpigmentation model (e.g., skin hyperpigmentationmodel 108) locally and generate, based on output of the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108), theuser-specific recommendation. The user computing device 111 c 1 may thenrender the user-specific recommendation on its display screen.

Additionally, or alternatively, in other embodiments, the imagingserver(s) 102 may analyze the user image remote from the user computingdevice to determine the user-specific skin hyperpigmentation valueand/or user-specific electronic recommendation designed to address atleast one feature identifiable within the pixel data comprising the atleast the portion of the user skin area. For example, in suchembodiments imaging server or a cloud-based computing platform (e.g.,imaging server(s) 102) receives, across computer network 120, the atleast one image comprising the pixel data of at the at least the portionof the user skin area. The server or a cloud-based computing platformmay then execute skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) and generate, based on output of the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108), theuser-specific recommendation. The server or a cloud-based computingplatform may then transmit, via the computer network (e.g., computernetwork 120), the user-specific recommendation to the user computingdevice for rendering on the display screen of the user computing device.

In some embodiments, the user may submit a new image to the skinhyperpigmentation model for analysis as described herein. In suchembodiments, one or more processors (e.g., imaging server(s) 102 and/ora user computing device, such as user computing device 111 c 1) mayreceive a new image of the user. The new image may be captured by adigital camera of user computing device 111 c 1. The new image maycomprise pixel data of at least a portion of a user skin area of theuser. The skin hyperpigmentation model (e.g., skin hyperpigmentationmodel 108) may then analyze, on the one or more processors (e.g.,imaging server(s) 102 and/or a user computing device, such as usercomputing device 111 c 1), the new image captured by the digital camerato determine a new user-specific skin hyperpigmentation value of theuser skin area. A new user-specific electronic recommendation or commentmay be generated, based on the new user-specific skin hyperpigmentationvalue, regarding at least one feature identifiable within the pixel dataof the new image. The new user-specific recommendation or comment (e.g.,message) may then be rendered on a display screen of a user computingdevice of the user.

In some embodiments, a user-specific electronic recommendation may bedisplayed on the display screen of a user computing device (e.g., usercomputing device 111 c 1) with a graphical representation of the user'sskin as annotated with one or more graphics or textual renderingscorresponding to the user-specific skin hyperpigmentation value. Instill further embodiments, the at least one user-specific electronicrecommendation may be rendered in real-time or near-real time during orafter receiving the at least one image having the user skin area.

In additional embodiments, a user-specific electronic recommendation maycomprise a product recommendation for a manufactured product. In suchembodiments, the user-specific electronic recommendation may bedisplayed on the display screen of a user computing device (e.g., usercomputing device 111 c 1) with instructions (e.g., a message) fortreating, with the manufactured product, the at least one featureidentifiable in the pixel data comprising the at least the portion ofthe user skin area. In still further embodiments, either the usercomputing device 111 c 1 and/or imaging server(s) may initiate, based onthe product recommendation, the manufactured product for shipment to theuser.

With regard to manufactured product recommendations, in someembodiments, one or more processors (e.g., imaging server(s) 102 and/ora user computing device, such as user computing device 111 c 1) maygenerate a modified image based on the at least one image of the user,e.g., as originally received. In such embodiments, the modified imagemay depict a rendering of how the user's skin is predicted to appearafter treating the at least one feature with the manufactured product.For example, the modified image may be modified by updating, smoothing,or changing colors of the pixels of the image to represent a possible orpredicted change after treatment of the at least one feature within thepixel data with the manufactured product. The modified image may then berendered on the display screen of the user computing device (e.g., usercomputing device 111 c 1).

Additionally, or alternatively, a recommendation may be also made forthe user's skin in the at least one image of the user, e.g., asoriginally received. In such embodiments, a user-specific electronicrecommendation may display on the display screen of the user computingdevice (e.g., user computing device 111 c 1) with instructions fortreating the at least one feature identifiable in the pixel datacomprising the at least the portion of the user skin area.

FIG. 4 illustrates an example user interface 402 as rendered on adisplay screen 400 of a user computing device 111 c 1 in accordance withvarious embodiments disclosed herein. For example, as shown in theexample of FIG. 4 , user interface 402 may be implemented or renderedvia an application (app) executing on user computing device 111 c 1.

For example, as shown in the example of FIG. 4 , user interface 402 maybe implemented or rendered via a native app executing on user computingdevice 111 c 1. In the example of FIG. 4 , user computing device 111 c 1is a user computer device as described for FIG. 1 , e.g., where 111 c 1is illustrated as an APPLE iPhone that implements the APPLE iOSoperating system and has display screen 400. User computing device 111 c1 may execute one or more native applications (apps) on its operatingsystem. Such native apps may be implemented or coded (e.g., as computinginstructions) in a computing language (e.g., SWIFT) executable by theuser computing device operating system (e.g., APPLE iOS) by theprocessor of user computing device 111 c 1.

Additionally, or alternatively, user interface 402 may be implemented orrendered via a web interface, such as via a web browser application,e.g., Safari and/or Google Chrome app(s), or other such web browser orthe like.

As shown in the example of FIG. 4 , user interface 402 comprises agraphical representation (e.g., image 202 a) of the user's skin. Image202 a may be the at least one image of the user (or graphicalrepresentation thereof), having pixels depicting skin hyperpigmentation,and as analyzed by the skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) as described herein. In the example of FIG.4 , graphical representation (e.g., image 202 a) of the user's skin isannotated with one or more graphics (e.g., area of pixel data 202 a 1)or textual rendering (e.g., text 202 at) corresponding to theuser-specific skin hyperpigmentation value. For example, the area ofpixel data 202 ap may be annotated or overlaid on top of the image ofthe user (e.g., image 202 a) to highlight the area or feature(s)identified within the pixel data (e.g., feature data and/or raw pixeldata) by the skin hyperpigmentation model (e.g., skin hyperpigmentationmodel 108). In the example of FIG. 4 , the area of pixel data 202 ap andthe feature(s) identified within include the user-specific skinhyperpigmentation of the user's skin area, and other features shown inarea of pixel data 202 ap. In various embodiments, the pixels identifiedas the specific features indicating skin hyperpigmentation (e.g., pixel202 ap 1 as a dark pixel indicating a dark amount of skin) from a bodyarea location (e.g., pixel 202 ap 2 positioned at a cheek of the user)may be highlighted or otherwise annotated when rendered.

Textual rendering (e.g., text 202 at) shows a user-specific skinhyperpigmentation value (e.g., 78.6%) which illustrates that the userhas a skin hyperpigmentation value of 78.6% in the region defined bypixel data 202 ap. The 78.6% value indicates that the user has a highamount of skin hyperpigmentation in the user skin area. It is to beunderstood that other textual rendering types or values are contemplatedherein, where textual rendering types or values may be rendered, forexample, as measurements, numerical values, amounts of pixels detectedas lax, or derivatives thereof, or the like. Additionally, oralternatively, color values may use and/or overlaid on a graphicalrepresentation shown on user interface 402 (e.g., image 202 a) toindicate a high degree of skin hyperpigmentation, a low degree of skinhyperpigmentation, or skin hyperpigmentation values within normal rangesor values (e.g., 25% to 50% skin hyperpigmentation value).

User interface 402 may also include or render a user-specific electronicrecommendation 412. In the embodiment of FIG. 4 , user-specificelectronic recommendation 412 comprises a message 412 m to the userdesigned to address at least one feature identifiable within the pixeldata comprising the at least the portion of the user skin area. As shownin the example of FIG. 4 , message 412 m recommends to the user to applya skin cream to the user's skin.

In particular, message 412 m recommends use of a skin cream to theuser's skin. The skin cream recommendation can be made based on the highskin hyperpigmentation value (e.g., 78.6%) as detected by the skinhyperpigmentation model where the skin cream product is designed toaddress the issue of skin hyperpigmentation detected in the pixel dataof image 202 a or otherwise assumed based on the high skinhyperpigmentation value. The product recommendation can be correlated tothe identified feature within the pixel data, and the user computingdevice 111 c 1 and/or server(s) 102 can be instructed to output theproduct recommendation when the feature (e.g., excessive skinhyperpigmentation) is identified.

User interface 402 also include or render a section for a productrecommendation 422 for a manufactured product 424 r (e.g., skin cream asdescribed above). The product recommendation 422 generally correspondsto the user-specific electronic recommendation 412, as described above.For example, in the example of FIG. 4 , the user-specific electronicrecommendation 412 is displayed on display screen 400 of user computingdevice 111 c 1 with instructions (e.g., message 412 m) for treating,with the manufactured product (manufactured product 424 r (e.g., skincream)) at least one feature (e.g., 78.6% skin hyperpigmentation atpixel 202 ap 1) identifiable in the pixel data (e.g., pixel data 202 ap)comprising the at least the portion of the user skin area (e.g., pixel202 ap 1).

As shown in FIG. 4 , user interface 402 recommends a product (e.g.,manufactured product 424 r (e.g., skin cream)) based on theuser-specific electronic recommendation 412. In the example of FIG. 4 ,the output or analysis of image(s) (e.g., image 202 a) of skinhyperpigmentation model (e.g., skin hyperpigmentation model 108), e.g.,user-specific electronic recommendation 412 and/or its related values(e.g., 78.6% skin hyperpigmentation) or related pixel data (e.g., 202 ap1 and/or 202 ap 2), may be used to generate or identify recommendationsfor corresponding product(s). Such recommendations may include productssuch as skin cream, cosmeceutical products, skin creams, and/or othersuch skin hyperpigmentation products, or the like, to address theuser-specific issue as detected within the pixel data by the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108).

In the example of FIG. 4 , user interface 402 renders or provides arecommended product (e.g., manufactured product 424 r) as determined byskin hyperpigmentation model (e.g., skin hyperpigmentation model 108)and its related image analysis of image 202 a and its pixel data andvarious features. In the example of FIG. 4 , this is indicated andannotated (424 p) on user interface 402.

User interface 402 may further include a selectable UI button 424 s toallow the user (e.g., the user of image 202 a) to select for purchase orshipment the corresponding product (e.g., manufactured product 424 r).In some embodiments, selection of selectable UI button 424 s a may causethe recommended product(s) to be shipped to the user (e.g., individual501) and/or may notify a third party that the individual is interestedin the product(s). For example, either user computing device 111 c 1and/or imaging server(s) 102 may initiate, based on user-specificelectronic recommendation 412, the manufactured product 424 r (e.g.,skin cream) for shipment to the user. In such embodiments, the productmay be packaged and shipped to the user.

In various embodiments, graphical representation (e.g., image 202 a),with graphical annotations (e.g., area of pixel data 202 ap), textualannotations (e.g., text 202 at), user-specific electronic recommendation412 may be transmitted, via the computer network (e.g., from an imagingserver 102 and/or one or more processors) to user computing device 111 c1, for rendering on display screen 400. In other embodiments, notransmission to the imaging server of the user's specific image occurs,where the user-specific recommendation (and/or product specificrecommendation) may instead be generated locally, by the skinhyperpigmentation model (e.g., skin hyperpigmentation model 108)executing and/or implemented on the user's mobile device (e.g., usercomputing device 111 c 1) and rendered, by a processor of the mobiledevice, on display screen 400 of the mobile device (e.g., user computingdevice 111 c 1).

In some embodiments, any one or more of graphical representations (e.g.,image 202 a), with graphical annotations (e.g., area of pixel data 202ap), textual annotations (e.g., text 202 at), user-specific electronicrecommendation 412, and/or product recommendation 422 may be rendered(e.g., rendered locally on display screen 400) in real-time or near-realtime during or after receiving the at least one image having the userskin area. In embodiments where the image is analyzed by imagingserver(s) 102, the image may be transmitted and analyzed in real-time ornear real-time by imaging server(s) 102.

In some embodiments, the user may provide a new image that may betransmitted to imaging server(s) 102 for updating, retraining, orreanalyzing by skin hyperpigmentation model 108. In other embodiments, anew image that may be locally received on computing device 111 c 1 andanalyzed, by skin hyperpigmentation model 108, on the computing device111 c 1.

In addition, as shown in the example of FIG. 4 , the user may selectselectable button 412 i to for reanalyzing (e.g., either locally atcomputing device 111 c 1 or remotely at imaging server(s) 102) a newimage. Selectable button 412 i may cause user interface 402 to promptthe user to attach for analyzing a new image. Imaging server(s) 102and/or a user computing device such as user computing device 111 c 1 mayreceive a new image of the user comprising pixel data of at least aportion of a user skin area of the user. The new image may be capturedby the digital camera. The new image (e.g., just like image 202 a) maycomprise pixel data of at least a portion of the user skin area. Theskin hyperpigmentation model (e.g., skin hyperpigmentation model 108),executing on the memory of the computing device (e.g., imaging server(s)102), may analyze the new image captured by the digital camera todetermine a new user-specific skin hyperpigmentation value of the user'sskin area. The computing device (e.g., imaging server(s) 102) maygenerate, based on the new user-specific skin hyperpigmentation value, anew user-specific electronic recommendation or comment regarding atleast one feature identifiable within the pixel data of the new image.For example, the new user-specific electronic recommendation may includea new graphical representation including graphics and/or text (e.g.,showing a new user-specific skin hyperpigmentation value, e.g., 60%).The new user-specific electronic recommendation may include additionalrecommendations, e.g., that the user has incorrectly applied a skincream as detected with the pixel data of the new image. A comment mayinclude that the user has corrected the at least one featureidentifiable within the pixel data (e.g., the user-specific skinhyperpigmentation value is now correct) by use of a recommendedmanufactured product or otherwise.

In some embodiments, a delta user-specific skin hyperpigmentation valuemay be generated, by the one or more processors (e.g., a processor ofimaging server(s) 102 and/or user computing device such as usercomputing device 111 c 1) based on a comparison between the newuser-specific skin hyperpigmentation value and the user-specific skinhyperpigmentation value. In such embodiments, the new user-specificrecommendation or comment may be further based on the deltauser-specific skin hyperpigmentation value. The delta user-specific skinhyperpigmentation value, a representation of the delta user-specificskin hyperpigmentation value (e.g., a graph or other graphicaldepiction), or a comment (e.g., text) based on the delta user-specificskin hyperpigmentation value, may be rendered on the display screen ofthe user computing device (e.g., user computing device 111 c 1) toillustrate or describe the difference (delta) between the newuser-specific skin hyperpigmentation value and the user-specific skinhyperpigmentation value as previously determined. Additionally, oralternatively, a delta user-specific skin hyperpigmentation value may begenerated based on a comparison between the new user-specific skinhyperpigmentation value and the user-specific skin hyperpigmentationvalue where the new user-specific recommendation comprises arecommendation of a hair removal product or hair removal technique forthe user corresponding to the delta user-specific skin hyperpigmentationvalue. As one example, the delta user-specific hyperpigmentation value,determined based on a first image captured at a first time and a secondimage captured at a second time, may indicate whether the user's skinwould benefit (e.g., experience less skin irritation and/or achieve acloser shave) from either a wet shaving razor, a dry shaving razor,and/or an electronic shaving razor, or based on other such razorcharacteristics. In such embodiments, the new user-specificrecommendation may display the recommendation for a shaving razor,specific to the user's skin hyperpigmentation value(s), on a displayscreen of the user computing screen. Additionally, or alternatively, asfurther examples, the user computing device, based on a deltauser-specific skin hyperpigmentation value for the user, may recommend arange of one or more hair removal product(s) or hair removaltechnique(s), which may include shaving using a wet razor, shaving usinga dry shaver, removing hair with epilators, waxes, and/or the like.

In various embodiments, the new user-specific recommendation or commentmay be transmitted via the computer network, from server(s) 102, to theuser computing device of the user for rendering on the display screen ofthe user computing device.

In other embodiments, no transmission to the imaging server of theuser's new image occurs, where the new user-specific recommendation(and/or product specific recommendation) may instead be generatedlocally, by the skin hyperpigmentation model (e.g., skinhyperpigmentation model 108) executing and/or implemented on the user'smobile device (e.g., user computing device 111 c 1) and rendered, by aprocessor of the mobile device, on a display screen of the mobile device(e.g., user computing device 111 c 1).

ASPECTS OF THE DISCLOSURE

The following aspects are provided as examples in accordance with thedisclosure herein and are not intended to limit the scope of thedisclosure.

1. A digital imaging method of analyzing pixel data of an image of askin area of a user for determining skin hyperpigmentation, the digitalimaging method comprising the steps of: (a) aggregating, at one or moreprocessors communicatively coupled to one or more memories, a pluralityof training images of a plurality of individuals, each of the trainingimages comprising pixel data of a skin area of a respective individual;(b) training, by the one or more processors with the pixel data of theplurality of training images, a skin hyperpigmentation model comprisinga skin hyperpigmentation scale and operable to output, across a range ofthe skin hyperpigmentation scale, skin hyperpigmentation valuesassociated with a degree of skin hyperpigmentation ranging from leasthyperpigmentation to most hyperpigmentation; (c) receiving, at the oneor more processors, at least one image of a user, the at least one imagecaptured by a digital camera, and the at least one image comprisingpixel data of at least a portion of a user skin area of the user; (d)analyzing, by the skin hyperpigmentation model executing on the one ormore processors, the at least one image captured by the digital camerato determine a user-specific skin hyperpigmentation value of the userskin area; (e) generating, by the one or more processors based on theuser-specific skin hyperpigmentation value, at least one user-specificelectronic recommendation designed to address at least one featureidentifiable within the pixel data comprising the at least the portionof the user skin area; and (f) rendering, on a display screen of a usercomputing device, the at least one user-specific recommendation.

2. The digital imaging method of aspect 1, wherein the at least oneuser-specific electronic recommendation is displayed on the displayscreen of the user computing device with a graphical representation ofthe user's skin as annotated with one or more graphics or textualrenderings corresponding to the user-specific skin hyperpigmentationvalue.

3. The digital imaging method of any one of aspects 1-2, wherein the atleast one user-specific electronic recommendation is rendered inreal-time or near-real time, during, or after receiving the at least oneimage having the user skin area.

4. The digital imaging method of any one of aspects 1-3, wherein the atleast one user-specific electronic recommendation comprises a productrecommendation for a manufactured product.

5. The digital imaging method of aspect 4, wherein the at least oneuser-specific electronic recommendation is displayed on the displayscreen of the user computing device with instructions for treating, withthe manufactured product, the at least one feature identifiable in thepixel data comprising the at least the portion of the user skin area.

6. The digital imaging method of aspect 4, further comprising the stepsof initiating, based on the product recommendation, the manufacturedproduct for shipment to the user.

7. The digital imaging method of aspect 4, further comprising the stepsof generating, by the one or more processors, a modified image based onthe at least one image, the modified image depicting how the user's skinis predicted to appear after treating the at least one feature with themanufactured product; and rendering, on the display screen of the usercomputing device, the modified image.

8. The digital imaging method of any one of aspects 1-7, wherein the atleast one user-specific electronic recommendation is displayed on thedisplay screen of the user computing device with instructions fortreating the at least one feature identifiable in the pixel datacomprising the at least the portion of the user skin area.

9. The digital imaging method of any one of aspects 1-8, wherein theskin hyperpigmentation model is an artificial intelligence (AI) basedmodel trained with at least one AI algorithm.

10. The digital imaging method of any one of aspects 1-9, wherein theskin hyperpigmentation model is further trained, by the one or moreprocessors with the pixel data of the plurality of training images, tooutput one or more location identifiers indicating one or morecorresponding body area locations of respective individuals, and whereinthe skin hyperpigmentation model, executing on the one or moreprocessors and analyzing the at least one image of the user, determinesa location identifier indicating a body area location of the user skinarea.

11. The digital method of aspect 10, wherein the body area locationcomprises the user's head, the user's groin, the user's underarm, theuser's cheek, the user's neck, the user's chest, the user's back, theuser's leg, the user's arm, or the user's bikini area.

12. The digital method of any one of aspects 1-11, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the skin hyperpigmentation model comprises training theskin hyperpigmentation model to detect a darker amount of skin from abody area location of the user to determine the user-specific skinhyperpigmentation value of the user skin area.

13. The digital method of any one of aspects 1-12, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the skin hyperpigmentation model comprises training theskin hyperpigmentation model to detect a darker amount of skin withinthe skin area to determine the user-specific skin hyperpigmentationvalue of the user skin area.

14. The digital method of any one of aspects 1-13, wherein training,wherein training, by the one or more processors with the pixel data ofthe plurality of training images, the skin hyperpigmentation modelcomprises training the skin hyperpigmentation model to detect a darkeramount of skin from a body area location of the user within the skinarea to determine the user-specific skin hyperpigmentation value of theuser skin area.

15. The digital method of any one of aspects 1-14, further comprising:receiving, at the one or more processors, a new image of the user, thenew image captured by the digital camera, and the new image comprisingpixel data of at least a portion of a user skin area of the user;analyzing, by the skin hyperpigmentation model executing on the one ormore processors, the new image captured by the digital camera todetermine a new user-specific skin hyperpigmentation value of the userskin area; generating, based on the new user-specific skinhyperpigmentation value, a new user-specific electronic recommendationor comment regarding at least one feature identifiable within the pixeldata of the new image; and rendering, on a display screen of a usercomputing device of the user, the new user-specific recommendation orcomment.

16. The digital imaging method of aspect 15, wherein a deltauser-specific skin hyperpigmentation value is generated based on acomparison between the new user-specific skin hyperpigmentation valueand the user-specific skin hyperpigmentation value, wherein the newuser-specific recommendation or comment is further based on the deltauser-specific skin hyperpigmentation value, and wherein the deltauser-specific skin hyperpigmentation value, a representation of thedelta user-specific skin hyperpigmentation value, or a comment based onthe delta user-specific skin hyperpigmentation value, is rendered on thedisplay screen of the user computing device.

17. The digital imaging method of aspect 15, wherein a deltauser-specific skin hyperpigmentation value is generated based on acomparison between the new user-specific skin hyperpigmentation valueand the user-specific skin hyperpigmentation value, wherein the newuser-specific recommendation comprises a recommendation of a hairremoval product or hair removal technique for the user corresponding tothe delta user-specific skin hyperpigmentation value.

18. The digital method of any one of aspects 1-17, wherein the one ormore processors comprises at least one of a server or a cloud-basedcomputing platform, and the server or the cloud-based computing platformreceives the plurality of training images of the plurality ofindividuals via a computer network, and wherein the server or thecloud-based computing platform trains the skin hyperpigmentation modelwith the pixel data of the plurality of training images.

19. The digital method of aspect 18, wherein the server or a cloud-basedcomputing platform receives the at least one image comprising the pixeldata of the at least the portion of the user skin area of the user, andwherein the server or a cloud-based computing platform executes the skinhyperpigmentation model and generates, based on output of the skinhyperpigmentation model, the user-specific recommendation and transmits,via the computer network, the user-specific recommendation to the usercomputing device for rendering on the display screen of the usercomputing device.

20. The digital method of any one of aspects 1-19, wherein the usercomputing device comprises at least one of a mobile device, a tablet, ahandheld device, a desktop device, a home assistant device, or apersonal assistant device.

21. The digital method of any one of aspects 1-20, wherein the usercomputing device receives the at least one image comprising the pixeldata of at least the portion of the user skin area of the user, andwherein the user computing device executes the skin hyperpigmentationmodel and generates, based on output of the skin hyperpigmentationmodel, the user-specific recommendation, and renders the user-specificrecommendation on the display screen of the user computing device.

22. The digital method of any one of aspects 1-22, wherein the at leastone image comprises a plurality of images.

23. The digital method of aspect 22, wherein the plurality of images arecollected using a digital video camera.

24. A digital imaging system configured to analyze pixel data of animage of a skin area of a user for determining skin hyperpigmentation,the digital imaging system comprising: an imaging server comprising aserver processor and a server memory; an imaging application (app)configured to execute on a user computing device comprising a deviceprocessor and a device memory, the imaging app communicatively coupledto the imaging server; and a skin hyperpigmentation model trained withpixel data of a plurality of training images of individuals and operableto output, across a range of a skin hyperpigmentation scale, skinhyperpigmentation values associated with a degree of skinhyperpigmentation ranging from least hyperpigmentation to mosthyperpigmentation, wherein the skin hyperpigmentation model isconfigured to execute on the server processor or the device processor tocause the server processor or the device processor to: receive, at theone or more processors, at least one image of a user, the at least oneimage captured by a digital camera, and the at least one imagecomprising pixel data of at least a portion of a user skin area of theuser; analyze, by the skin hyperpigmentation model executing on the oneor more processors, the at least one image captured by the digitalcamera to determine a user-specific skin hyperpigmentation value of theuser skin area; generate, by the one or more processors based on theuser-specific skin hyperpigmentation value, at least one user-specificelectronic recommendation designed to address at least one featureidentifiable within the pixel data comprising the at least the portionof the user skin area; and render, on a display screen of a usercomputing device, the at least one user-specific recommendation.

25. A tangible, non-transitory computer-readable medium storinginstructions for analyzing pixel data of an image of a skin area of auser for determining skin hyperpigmentation, that when executed by oneor more processors cause the one or more processors to: (a) aggregate,at one or more processors communicatively coupled to one or morememories, a plurality of training images of a plurality of individuals,each of the training images comprising pixel data of a skin area of arespective individual; (b) train, by the one or more processors with thepixel data of the plurality of training images, a skin hyperpigmentationmodel comprising a skin hyperpigmentation scale and operable to output,across a range of the skin hyperpigmentation scale, skinhyperpigmentation values associated with a degree of skinhyperpigmentation ranging from least hyperpigmentation to mosthyperpigmentation; (c) receive, at the one or more processors, at leastone image of a user, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of a user skin area of the user; (d) analyze, by the skinhyperpigmentation model executing on the one or more processors, the atleast one image captured by the digital camera to determine auser-specific skin hyperpigmentation value of the user skin area; (e)generate, by the one or more processors based on the user-specific skinhyperpigmentation value, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the userskin area; and (f) render, on a display screen of a user computingdevice, the at least one user-specific recommendation.

ADDITIONAL CONSIDERATIONS

Although the disclosure herein sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location, while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In otherembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. A person of ordinaryskill in the art may implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this application.

Those of ordinary skill in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality and improve the functioning of conventionalcomputers.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

Every document cited herein, including any cross referenced or relatedpatent or application and any patent application or patent to which thisapplication claims priority or benefit thereof, is hereby incorporatedherein by reference in its entirety unless expressly excluded orotherwise limited. The citation of any document is not an admission thatit is prior art with respect to any invention disclosed or claimedherein or that it alone, or in any combination with any other referenceor references, teaches, suggests or discloses any such invention.Further, to the extent that any meaning or definition of a term in thisdocument conflicts with any meaning or definition of the same term in adocument incorporated by reference, the meaning or definition assignedto that term in this document shall govern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. A digital imaging method of analyzing pixel dataof an image of a skin area of a user for determining skinhyperpigmentation, the digital imaging method comprising the steps of:a. aggregating, at one or more processors communicatively coupled to oneor more memories, a plurality of training images of a plurality ofindividuals, each of the training images comprising pixel data of a skinarea of a respective individual; b. training, by the one or moreprocessors with the pixel data of the plurality of training images, askin hyperpigmentation model comprising a skin hyperpigmentation scaleand operable to output, across a range of the skin hyperpigmentationscale, skin hyperpigmentation values associated with a degree of skinhyperpigmentation ranging from least hyperpigmentation to mosthyperpigmentation; c. receiving, at the one or more processors, at leastone image of a user, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of a user skin area of the user; d. analyzing, by the skinhyperpigmentation model executing on the one or more processors, the atleast one image captured by the digital camera to determine auser-specific skin hyperpigmentation value of the user skin area; e.generating, by the one or more processors based on the user-specificskin hyperpigmentation value, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the userskin area; and f. rendering, on a display screen of a user computingdevice, the at least one user-specific recommendation.
 2. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation is displayed on the display screen of the usercomputing device with a graphical representation of the user's skin asannotated with one or more graphics or textual renderings correspondingto the user-specific skin hyperpigmentation value.
 3. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation is rendered in real-time or near-real time,during, or after receiving the at least one image having the user skinarea.
 4. The digital imaging method of claim 1, wherein the at least oneuser-specific electronic recommendation comprises a productrecommendation for a manufactured product.
 5. The digital imaging methodof claim 4, wherein the at least one user-specific electronicrecommendation is displayed on the display screen of the user computingdevice with instructions for treating, with the manufactured product,the at least one feature identifiable in the pixel data comprising theat least the portion of the user skin area
 6. The digital imaging methodof claim 4, further comprising the steps of: initiating, based on theproduct recommendation, the manufactured product for shipment to theuser.
 7. The digital imaging method of claim 4, further comprising thesteps of: generating, by the one or more processors, a modified imagebased on the at least one image, the modified image depicting how theuser's skin is predicted to appear after treating the at least onefeature with the manufactured product; and rendering, on the displayscreen of the user computing device, the modified image.
 8. The digitalimaging method of claim 1, wherein the at least one user-specificelectronic recommendation is displayed on the display screen of the usercomputing device with instructions for treating the at least one featureidentifiable in the pixel data comprising the at least the portion ofthe user skin area.
 9. The digital imaging method of claim 1, whereinthe skin hyperpigmentation model is an artificial intelligence (AI)based model trained with at least one AI algorithm.
 10. The digitalimaging method of claim 1, wherein the skin hyperpigmentation model isfurther trained, by the one or more processors with the pixel data ofthe plurality of training images, to output one or more locationidentifiers indicating one or more corresponding body area locations ofrespective individuals, and wherein the skin hyperpigmentation model,executing on the one or more processors and analyzing the at least oneimage of the user, determines a location identifier indicating a bodyarea location of the user skin area.
 11. The digital method of claim 10,wherein the body area location comprises the user's cheek, the user'sneck, the user's head, the user's groin, the user's underarm, the user'schest, the user's back, the user's leg, the user's arm, or the user'sbikini area.
 12. The digital method of claim 1, wherein training, by theone or more processors with the pixel data of the plurality of trainingimages, the skin hyperpigmentation model comprises training the skinhyperpigmentation model to detect a darker amount of skin from a bodyarea location of the user to determine the user-specific skinhyperpigmentation value of the user skin area.
 13. The digital method ofclaim 1, wherein training, by the one or more processors with the pixeldata of the plurality of training images, the skin hyperpigmentationmodel comprises training the skin hyperpigmentation model to detect adarker amount of skin within the skin area to determine theuser-specific skin hyperpigmentation value of the user skin area. 14.The digital method of claim 1, wherein training, wherein training, bythe one or more processors with the pixel data of the plurality oftraining images, the skin hyperpigmentation model comprises training theskin hyperpigmentation model to detect a dark amount of skin from a bodyarea location of the user within the skin area to determine theuser-specific skin hyperpigmentation value of the user skin area. 15.The digital method of claim 1, further comprising: receiving, at the oneor more processors, a new image of the user, the new image captured bythe digital camera, and the new image comprising pixel data of at leasta portion of a user skin area of the user; analyzing, by the skinhyperpigmentation model executing on the one or more processors, the newimage captured by the digital camera to determine a new user-specificskin hyperpigmentation value of the user skin area; generating, based onthe new user-specific skin hyperpigmentation value, a new user-specificelectronic recommendation or comment regarding at least one featureidentifiable within the pixel data of the new image; and rendering, on adisplay screen of a user computing device of the user, the newuser-specific recommendation or comment.
 16. The digital imaging methodof claim 15, wherein a delta user-specific skin hyperpigmentation valueis generated based on a comparison between the new user-specific skinhyperpigmentation value and the user-specific skin hyperpigmentationvalue, wherein the new user-specific recommendation or comment isfurther based on the delta user-specific skin hyperpigmentation value,and wherein the delta user-specific skin hyperpigmentation value, arepresentation of the delta user-specific skin hyperpigmentation value,or a comment based on the delta user-specific skin hyperpigmentationvalue, is rendered on the display screen of the user computing device.17. The digital imaging method of claim 15, wherein a deltauser-specific skin hyperpigmentation value is generated based on acomparison between the new user-specific skin hyperpigmentation valueand the user-specific skin hyperpigmentation value, wherein the newuser-specific recommendation comprises a recommendation of a hairremoval product or hair removal technique for the user corresponding tothe delta user-specific skin hyperpigmentation value.
 18. The digitalmethod of claim 1, wherein the user computing device receives the atleast one image the user-specific recommendation on the display screenof the user computing device.
 19. A digital imaging system configured toanalyze pixel data of an image of a skin area of a user for determiningskin hyperpigmentation, the digital imaging system comprising: animaging server comprising a server processor and a server memory; animaging application (app) configured to execute on a user computingdevice comprising a device processor and a device memory, the imagingapp communicatively coupled to the imaging server; and a skinhyperpigmentation model trained with pixel data of a plurality oftraining images of individuals and operable to output, across a range ofa skin hyperpigmentation scale, skin hyperpigmentation values associatedwith a degree of skin hyperpigmentation ranging from leasthyperpigmentation to most hyperpigmentation, wherein the skinhyperpigmentation model is configured to execute on the server processoror the device processor to cause the server processor or the deviceprocessor to: receive, at the one or more processors, at least one imageof a user, the at least one image captured by a digital camera, and theat least one image comprising pixel data of at least a portion of a userskin area of the user; analyze, by the skin hyperpigmentation modelexecuting on the one or more processors, the at least one image capturedby the digital camera to determine a user-specific skinhyperpigmentation value of the user skin area; generate, by the one ormore processors based on the user-specific skin hyperpigmentation value,at least one user-specific electronic recommendation designed to addressat least one feature identifiable within the pixel data comprising theat least the portion of the user skin area; and render, on a displayscreen of a user computing device, the at least one user-specificrecommendation.
 20. A tangible, non-transitory computer-readable mediumstoring instructions for analyzing pixel data of an image of a skin areaof a user for determining skin hyperpigmentation, that when executed byone or more processors cause the one or more processors to: a.aggregate, at one or more processors communicatively coupled to one ormore memories, a plurality of training images of a plurality ofindividuals, each of the training images comprising pixel data of a skinarea of a respective individual; b. train, by the one or more processorswith the pixel data of the plurality of training images, a skinhyperpigmentation model comprising a skin hyperpigmentation scale andoperable to output, across a range of the skin hyperpigmentation scale,skin hyperpigmentation values associated with a degree of skinhyperpigmentation ranging from least hyperpigmentation to mosthyperpigmentation; c. receive, at the one or more processors, at leastone image of a user, the at least one image captured by a digitalcamera, and the at least one image comprising pixel data of at least aportion of a user skin area of the user; d. analyze, by the skinhyperpigmentation model executing on the one or more processors, the atleast one image captured by the digital camera to determine auser-specific skin hyperpigmentation value of the user skin area; e.generate, by the one or more processors based on the user-specific skinhyperpigmentation value, at least one user-specific electronicrecommendation designed to address at least one feature identifiablewithin the pixel data comprising the at least the portion of the userskin area; and f. render, on a display screen of a user computingdevice, the at least one user-specific recommendation.